The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs.

Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on predicting a limited number of maintenance needs, predominantly based solely on vehicle mileage and driving time. This approach often falls short, as it does not comprehensively monitor the overall health condition of vehicles, thus posing potential safety risks. To address this issue, we propose a deep fusion network model that utilizes multi-source data, including vehicle maintenance record data and vehicle base information data, to provide comprehensive predictions for vehicle maintenance projects. To capture the relationships among various maintenance projects, we create a correlation representation using the maintenance project co-occurrence matrix. Furthermore, building on the correlation representation, we propose a deep fusion network that employs the attention mechanism to efficiently merge vehicle mileage and vehicle base information. Experiments conducted on real data demonstrate the superior performance of our proposed model relative to competitive baseline models in predicting vehicle maintenance projects.

It is of paramount importance to accurately predict the maintenance demands of vehicles in order to guarantee their sustainable use. Nevertheless, the current methodologies merely predict a partial aspect of a vehicle’s maintenance demands, rather than the comprehensive maintenance demands. Moreover, the process of predicting vehicle maintenance demands must give due consideration to the influence of mileage on such demands. In light of the aforementioned considerations, we put forth a vehicle overall maintenance demand prediction method that incorporates vehicle mileage awareness. In order to address the discrepancy between the vector space of mileage and that of the project, we put forth a mileage representation method for the maintenance demand prediction task. To capture the significant impact of key mileage and projects on future demand, we propose a learning module for key temporal information using a fusion of Long Short-Term Memory (LSTM) networks and attention mechanism. Moreover, to integrate maintenance mileage and projects, we propose a fusion method based on a gated unit. The experimental results obtained from real datasets demonstrate that the proposed model exhibits a superior performance compared to existing methods.

To analyze the influence of structure type on passing ability of car transport vehicle combination, the four basic vehicle combination models of two typical structure types, such as car transport semi-trailer road train and car transport vehicle combination with center axle trailer, have been built by using TruckSim. The turning circle test and the adaptability test on highway have been simulated on dry pavement to analyze the relationship between the structure type and the turning passing ability. The results show that the structure parameters have significant influence on the passing ability of car transport vehicle combination; three of the vehicle combination models can meet the requirements of GB 1589-2004“Limits of dimensions, axle load and masses for road vehicles”; all the four basic vehicle combination models will take up the lane beside itself when running on the up to and including third-grade highway/ramp.

To analyze the influence of structure parameters on the road adaptability of double semi-trailer road trains, we built two double semi-trailer road train (B-train) models using TrackSim. The turning circle test and the road adaptability test were simulated on dry pavement to analyze the relationship between the structure parameters and the turning passing ability. The results show that the structure parameters have significant influence on the road adaptability of double semi-trailer road trains. A B-Train cannot meet the GB 1589-2004 national standard of limits of dimensions, axle load, and masses for road vehicles. However, a B-Train with a dolly and with the same length can meet the requirements, and the passing ability is better. Both double semi-trailer road train models will take up a lane beside itself when miming up to and including third-grade highway/ramps.